| # Pytorch ResNet18 QAT | |
| ## Model Source | |
| The model used in this example come from the 'torchvision', more details in the 'export_pytorch_model' function of the script. | |
| ## Script Usage | |
| *Usage:* | |
| ``` | |
| python test.py | |
| ``` | |
| *rknn_convert usage:* | |
| ``` | |
| python3 -m rknn.api.rknn_convert -t rk3568 -i ./model_config.yml -o ./ | |
| ``` | |
| *Description:* | |
| - The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. | |
| - If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. | |
| - This is a QAT model, and the do_quantization of rknn.build needs to be set to False. | |
| ## Expected Results | |
| This example will print the TOP5 labels and corresponding scores of the test image classification results, as follows: | |
| ``` | |
| -----TOP 5----- | |
| [812] score:0.999741 class:"space shuttle" | |
| [404] score:0.000194 class:"airliner" | |
| [657] score:0.000015 class:"missile" | |
| [466] score:0.000008 class:"bullet train, bullet" | |
| [744] score:0.000008 class:"projectile, missile" | |
| ``` | |
| - Note: Different platforms, different versions of tools and drivers may have slightly different results. |